带动量的Gradient Descent表现逊于普通GD，请求排查问题

Hey there, I’ve been in this exact spot before when rolling out momentum GD—super frustrating when a supposed "better" optimizer tanks your performance! Let’s break down the most common issues that could be causing your drop from 85% to 62% accuracy:

• Momentum hyperparameter (β) is misconfigured
  The standard default β is 0.9, but values that are too high (like 0.999) or too low (close to 0) can derail training. A β that’s too high makes the momentum term accumulate too much outdated gradient info, leading the model to overshoot loss minima. Too low, and it’s basically vanilla GD with extra unnecessary steps. Start by tuning β between 0.8 and 0.95, and log the magnitude of weight updates to see if they’re exploding or staying too small.

• Learning rate (α) wasn’t adjusted for momentum
  Momentum GD almost always needs a lower learning rate than vanilla GD. Since momentum smooths out gradient fluctuations, a rate that worked for vanilla GD might be too aggressive here, causing unstable training and poor convergence. Try scaling your learning rate down by 2-10x (e.g., if you used 0.01 for GD, test 0.001-0.005 for momentum GD) and see if that fixes things.

• Momentum update logic has bugs
  Double-check your code against the standard momentum implementation—common mistakes here break everything:

  # Correct momentum update
  v = β * v + (1 - β) * dW  # dW is weight gradient; (1-β) fixes initial bias
  W = W - α * v
  

  Watch out for forgetting the (1 - β) correction, updating v after applying the weight change instead of before, or initializing v to the wrong shape (it needs to match your weights’ shape, initialized to zeros).

• Training loop or stopping criteria don’t fit momentum
  Momentum GD usually converges faster than vanilla GD, but if you’re using the same epoch count or early stopping rules that worked for vanilla GD, you might be stopping too early (before the model settles) or too late (allowing it to overshoot and diverge). Plot your training loss curves: if the momentum version’s loss oscillates wildly, that’s a sign of too high α or β. If it plateaus early but stays higher than vanilla GD’s loss, you might need more epochs.

• Conflicting momentum terms in other layers
  If your model uses batch normalization, make sure you’re not mixing up the batch norm’s momentum parameter with your optimizer’s momentum—they’re completely separate! Using the same β value for both can cause unexpected training dynamics. Also, confirm your data preprocessing (normalization, shuffling, train/test splits) is identical between the two runs—even a small difference here can lead to big accuracy gaps.

Start with checking the learning rate and β first—those are the most frequent culprits. If you can share a snippet of your momentum GD code, that’d help narrow things down even further, but these steps should cover most cases.

内容的提问来源于stack exchange，提问作者Frixoe